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In [[mathematics]], more specifically in [[numerical linear algebra]], the '''biconjugate gradient method''' is an [[algorithm]] to solve [[system of linear equations|systems of linear equations]] | |||
:<math>A x= b.\,</math> | |||
Unlike the [[conjugate gradient method]], this algorithm does not require the [[matrix (mathematics)|matrix]] <math>A</math> to be [[self-adjoint]], but instead one needs to perform multiplications by the [[conjugate transpose]] {{math|<var>A</var><sup>*</sup>}}. | |||
==The algorithm== | |||
# Choose initial guess <math>x_0\,</math>, two other vectors <math>x_0^*</math> and <math>b^*\,</math> and a [[preconditioner]] <math>M\,</math> | |||
# <math>r_0 \leftarrow b-A\, x_0\,</math> | |||
# <math>r_0^* \leftarrow b^*-x_0^*\, A^T </math> | |||
# <math>p_0 \leftarrow M^{-1} r_0\,</math> | |||
# <math>p_0^* \leftarrow r_0^*M^{-1}\,</math> | |||
# for <math>k=0, 1, \ldots</math> do | |||
## <math>\alpha_k \leftarrow {r_k^* M^{-1} r_k \over p_k^* A p_k}\,</math> | |||
## <math>x_{k+1} \leftarrow x_k + \alpha_k \cdot p_k\,</math> | |||
## <math>x_{k+1}^* \leftarrow x_k^* + \overline{\alpha_k}\cdot p_k^*\,</math> | |||
## <math>r_{k+1} \leftarrow r_k - \alpha_k \cdot A p_k\,</math> | |||
## <math>r_{k+1}^* \leftarrow r_k^*- \overline{\alpha_k} \cdot p_k^*\, A </math> | |||
## <math>\beta_k \leftarrow {r_{k+1}^* M^{-1} r_{k+1} \over r_k^* M^{-1} r_k}\,</math> | |||
## <math>p_{k+1} \leftarrow M^{-1} r_{k+1} + \beta_k \cdot p_k\,</math> | |||
## <math>p_{k+1}^* \leftarrow r_{k+1}^*M^{-1} + \overline{\beta_k}\cdot p_k^*\,</math> | |||
In the above formulation, the computed <math>r_k\,</math> and <math>r_k^*</math> satisfy | |||
:<math>r_k = b - A x_k,\,</math> | |||
:<math>r_k^* = b^* - x_k^*\, A </math> | |||
and thus are the respective [[Residual (numerical analysis)|residual]]s corresponding to <math>x_k\,</math> and <math>x_k^*</math>, as approximate solutions to the systems | |||
:<math>A x = b,\,</math> | |||
:<math>x^*\, A = b^*\,;</math> | |||
<math>x^*</math> is the [[Hermitian adjoint|adjoint]], and <math>\overline{\alpha}</math> is the [[complex conjugate]]. | |||
=== Unpreconditioned version of the algorithm === | |||
# Choose initial guess <math>x_0\,</math>, | |||
# <math>r_0 \leftarrow b-A\, x_0\,</math> | |||
# <math>\hat{r}_0 \leftarrow \hat{b} - \hat{x}_0A^T </math> | |||
# <math>p_0 \leftarrow r_0\,</math> | |||
# <math>\hat{p}_0 \leftarrow \hat{r}_0\,</math> | |||
# for <math>k=0, 1, \ldots</math> do | |||
## <math>\alpha_k \leftarrow {\hat{r}_k r_k \over \hat{p}_k A p_k}\,</math> | |||
## <math>x_{k+1} \leftarrow x_k + \alpha_k \cdot p_k\,</math> | |||
## <math>\hat{x}_{k+1} \leftarrow \hat{x}_k + \alpha_k \cdot \hat{p}_k\,</math> | |||
## <math>r_{k+1} \leftarrow r_k - \alpha_k \cdot A p_k\,</math> | |||
## <math>\hat{r}_{k+1} \leftarrow \hat{r}_k- \alpha_k \cdot \hat{p}_k A^T </math> | |||
## <math>\beta_k \leftarrow {\hat{r}_{k+1} r_{k+1} \over \hat{r}_k r_k}\,</math> | |||
## <math>p_{k+1} \leftarrow r_{k+1} + \beta_k \cdot p_k\,</math> | |||
## <math>\hat{p}_{k+1} \leftarrow \hat{r}_{k+1} + \beta_k \cdot \hat{p}_k\,</math> | |||
==Discussion== | |||
The biconjugate gradient method is [[numerical stability|numerically unstable]]{{Citation needed|date=September 2009}} (compare to the [[biconjugate gradient stabilized method]]), but very important from a theoretical point of view. Define the iteration steps by | |||
:<math>x_k:=x_j+ P_k A^{-1}\left(b - A x_j \right),</math> | |||
:<math>x_k^*:= x_j^*+\left(b^*- x_j^* A \right) P_k A^{-1},</math> | |||
where <math>j<k</math> using the related [[projection (linear algebra)|projection]] | |||
:<math>P_k:= \mathbf{u}_k \left(\mathbf{v}_k^* A \mathbf{u}_k \right)^{-1} \mathbf{v}_k^* A,</math> | |||
with | |||
:<math>\mathbf{u}_k=\left[u_0, u_1, \dots, u_{k-1} \right],</math> | |||
:<math>\mathbf{v}_k=\left[v_0, v_1, \dots, v_{k-1} \right].</math> | |||
These related projections may be iterated themselves as | |||
:<math>P_{k+1}= P_k+ \left( 1-P_k\right) u_k \otimes {v_k^* A\left(1-P_k \right) \over v_k^* A\left(1-P_k \right) u_k}.</math> | |||
A relation to [[Quasi-Newton method]]s is given by <math>P_k= A_k^{-1} A</math> and <math>x_{k+1}= x_k- A_{k+1}^{-1}\left(A x_k -b \right)</math>, where | |||
:<math>A_{k+1}^{-1}= A_k^{-1}+ \left( 1-A_k^{-1}A\right) u_k \otimes {v_k^* \left(1-A A_k^{-1} \right) \over v_k^* A\left(1-A_k^{-1}A \right) u_k}.</math> | |||
The new directions | |||
:<math>p_k = \left(1-P_k \right) u_k,</math> | |||
:<math>p_k^* = v_k^* A \left(1- P_k \right) A^{-1}</math> | |||
are then orthogonal to the residuals: | |||
:<math>v_i^* r_k= p_i^* r_k=0,</math> | |||
:<math>r_k^* u_j = r_k^* p_j= 0,</math> | |||
which themselves satisfy | |||
:<math>r_k= A \left( 1- P_k \right) A^{-1} r_j,</math> | |||
:<math>r_k^*= r_j^* \left( 1- P_k \right)</math> | |||
where <math>i,j<k</math>. | |||
The biconjugate gradient method now makes a special choice and uses the setting | |||
:<math>u_k = M^{-1} r_k,\,</math> | |||
:<math>v_k^* = r_k^* \, M^{-1}.\,</math> | |||
With this particular choice, explicit evaluations of <math>P_k</math> and {{math|<var>A</var><sup>−1</sup>}} are avoided, and the algorithm takes the form stated above. | |||
==Properties== | |||
* If <math>A= A^*\,</math> is [[Conjugate transpose|self-adjoint]], <math>x_0^*= x_0</math> and <math>b^*=b</math>, then <math>r_k= r_k^*</math>, <math>p_k= p_k^*</math>, and the [[conjugate gradient method]] produces the same sequence <math>x_k= x_k^*</math> at half the computational cost. | |||
* The sequences produced by the algorithm are [[Biorthogonal system|biorthogonal]], i.e., <math>p_i^*Ap_j=r_i^*M^{-1}r_j=0</math> for <math>i \neq j</math>. | |||
* if <math>P_{j'}\,</math> is a polynomial with <math>\mathrm{deg}\left(P_{j'}\right)+j<k</math>, then <math>r_k^*P_{j'}\left(M^{-1}A\right)u_j=0</math>. The algorithm thus produces projections onto the [[Krylov subspace]]. | |||
* if <math>P_{i'}\,</math> is a polynomial with <math>i+\mathrm{deg}\left(P_{i'}\right)<k</math>, then <math>v_i^*P_{i'}\left(AM^{-1}\right)r_k=0</math>. | |||
==See also== | |||
* [[Biconjugate gradient stabilized method]] | |||
* [[Conjugate gradient method]] | |||
==References== | |||
* {{cite journal|first=R.|last=Fletcher|year=1976|title=Conjugate gradient methods for indefinite systems|journal=Numerical Analysis|volume=506|series=Lecture Notes in Mathematics|publisher=Springer Berlin / Heidelberg|issn=1617-9692|isbn=978-3-540-07610-0|pages=73–89|url=http://www.springerlink.com/content/974t1l33m84217um/|doi=10.1007/BFb0080109|editor1-last=Watson|editor1-first=G. Alistair}} | |||
* {{Cite book |last1=Press|first1=WH|last2=Teukolsky|first2=SA|last3=Vetterling|first3=WT|last4=Flannery|first4=BP|year=2007|title=Numerical Recipes: The Art of Scientific Computing|edition=3rd|publisher=Cambridge University Press| publication-place=New York|isbn=978-0-521-88068-8|chapter=Section 2.7.6|chapter-url=http://apps.nrbook.com/empanel/index.html?pg=87 |postscript=<!-- Bot inserted parameter. Either remove it; or change its value to "." for the cite to end in a ".", as necessary. -->{{inconsistent citations}}}} | |||
{{Numerical linear algebra}} | |||
[[Category:Numerical linear algebra]] | |||
[[Category:Gradient methods]] |
Revision as of 03:28, 2 December 2013
In mathematics, more specifically in numerical linear algebra, the biconjugate gradient method is an algorithm to solve systems of linear equations
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The algorithm
- Choose initial guess , two other vectors and and a preconditioner
- for do
In the above formulation, the computed and satisfy
and thus are the respective residuals corresponding to and , as approximate solutions to the systems
is the adjoint, and is the complex conjugate.
Unpreconditioned version of the algorithm
Discussion
The biconjugate gradient method is numerically unstablePotter or Ceramic Artist Truman Bedell from Rexton, has interests which include ceramics, best property developers in singapore developers in singapore and scrabble. Was especially enthused after visiting Alejandro de Humboldt National Park. (compare to the biconjugate gradient stabilized method), but very important from a theoretical point of view. Define the iteration steps by
where using the related projection
with
These related projections may be iterated themselves as
A relation to Quasi-Newton methods is given by and , where
The new directions
are then orthogonal to the residuals:
which themselves satisfy
The biconjugate gradient method now makes a special choice and uses the setting
With this particular choice, explicit evaluations of and Buying, selling and renting HDB and personal residential properties in Singapore are simple and transparent transactions. Although you are not required to engage a real property salesperson (generally often known as a "public listed property developers In singapore agent") to complete these property transactions, chances are you'll think about partaking one if you are not accustomed to the processes concerned.
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There isn't any deal too small. Property agents who are keen to find time for any deal even if the commission is small are the ones you want on your aspect. Additionally they present humbleness and might relate with the typical Singaporean higher. Relentlessly pursuing any deal, calling prospects even without being prompted. Even if they get rejected a hundred times, they still come again for more. These are the property brokers who will find consumers what they need eventually, and who would be the most successful in what they do. 4. Honesty and Integrity
This feature is suitable for you who need to get the tax deductions out of your PIC scheme to your property agency firm. It's endorsed that you visit the correct site for filling this tax return software. This utility must be submitted at the very least yearly to report your whole tax and tax return that you're going to receive in the current accounting 12 months. There may be an official website for this tax filling procedure. Filling this tax return software shouldn't be a tough thing to do for all business homeowners in Singapore.
A wholly owned subsidiary of SLP Worldwide, SLP Realty houses 900 associates to service SLP's fast rising portfolio of residential tasks. Real estate is a human-centric trade. Apart from offering comprehensive coaching applications for our associates, SLP Realty puts equal emphasis on creating human capabilities and creating sturdy teamwork throughout all ranges of our organisational hierarchy. Worldwide Presence At SLP International, our staff of execs is pushed to make sure our shoppers meet their enterprise and investment targets. Under is an inventory of some notable shoppers from completely different industries and markets, who've entrusted their real estate must the expertise of SLP Worldwide.
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Another method by way of which you could find out whether the agent is sweet is by checking the feedback, of the shoppers, on the website. There are various individuals would publish their comments on the web site of the Singapore property agent. You can take a look at these feedback and the see whether it will be clever to hire that specific Singapore property agent. You may even get in contact with the developer immediately. Many Singapore property brokers know the developers and you may confirm the goodwill of the agent by asking the developer. are avoided, and the algorithm takes the form stated above.
Properties
- If is self-adjoint, and , then , , and the conjugate gradient method produces the same sequence at half the computational cost.
- The sequences produced by the algorithm are biorthogonal, i.e., for .
- if is a polynomial with , then . The algorithm thus produces projections onto the Krylov subspace.
See also
References
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